An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data

2026-09-02

AI Summary

本文针对停车空间分类数据不足的问题，提出了一种基于自学习框架和卷积自动编码器的方法，通过无监督表示学习及异构自动编码器集成提高分类准确性和鲁棒性。

Abstract

Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learning approaches rely on large amounts of annotated data and exhibit limited generalization across heterogeneous environments. To address these limitations, we investigate a self-taught learning framework based on unsupervised representation learning with convolutional autoencoders. The proposed approach learns transferable visual representations from unlabeled data and reuses the learned encoders as fixed feature extractors for supervised classification with limited annotated samples in the target domain. To further enhance robustness and mitigate architectural bias, an ensemble of heterogeneous autoencoders is employed, with independent classifier heads and prediction fusion at inference time. Experiments conducted on the PKLot and CNRPark benchmarks under cross-dataset evaluation protocols show that the proposed ensemble-based strategy substantially reduces annotation requirements while improving robustness under significant domain shifts, achieving accuracies between 93\% and 96\% in data-constrained scenarios.

Problem

• Parking Space Classification
• Limited Data
• Generalization
• Intelligent Transportation Systems

Innovation

• self-taught learning
• unsupervised representation learning
• convolutional autoencoders
• ensemble of heterogeneous autoencoders
• limited annotated data